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library(nycflights13)
library(tidyverse)

Take careful note of the conflicts message that’s printed when you load the tidyverse. It tells you that dplyr overwrites some functions in base R. If you want to use the base version of these functions after loading dplyr, you’ll need to use their full names: stats::filter() and stats::lag(). So far we’ve mostly ignored which package a function comes from because most of the time it doesn’t matter. However, knowing the package can help you find help and find related functions, so when we need to be precise about which package a function comes from, we’ll use the same syntax as R: packagename::functionname().

3.1.2 nycflights13

To explore the basic dplyr verbs, we’re going to use nycflights13::flights. This dataset contains all 336,776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics, and is documented in ?flights.

flights is a tibble, a special type of data frame used by the tidyverse to avoid some common gotchas. The most important difference between tibbles and data frames is the way tibbles print; they are designed for large datasets, so they only show the first few rows and only the columns that fit on one screen. There are a few options to see everything. If you’re using RStudio, the most convenient is probably View(flights), which will open an interactive scrollable and filterable view. Otherwise you can use print(flights, width = Inf) to show all columns, or use glimpse()

3.1.3 dplyr basics

You’re about to learn the primary dplyr verbs (functions) which will allow you to solve the vast majority of your data manipulation challenges. But before we discuss their individual differences, it’s worth stating what they have in common:

The first argument is always a data frame.

The subsequent arguments typically describe which columns to operate on, using the variable names (without quotes).

The output is always a new data frame.

Because each verb does one thing well, solving complex problems will usually require combining multiple verbs, and we’ll do so with the pipe, |>. We’ll discuss the pipe more in Section 3.4, but in brief, the pipe takes the thing on its left and passes it along to the function on its right so that x |> f(y) is equivalent to f(x, y), and x |> f(y) |> g(z) is equivalent to g(f(x, y), z). The easiest way to pronounce the pipe is “then”. That makes it possible to get a sense of the following code even though you haven’t yet learned the details:

flights |>
  filter(dest == "IAH") |> 
  group_by(year, month, day) |> 
  summarize(
    arr_delay = mean(arr_delay, na.rm = TRUE)
  )

`summarise()` has grouped output by 'year', 'month'. You can override using the `.groups` argument.

dplyr’s verbs are organized into four groups based on what they operate on: rows, columns, groups, or tables. In the following sections you’ll learn the most important verbs for rows, columns, and groups, then we’ll come back to the join verbs that work on tables in Chapter 19. Let’s dive in!

3.2 Rows

The most important verbs that operate on rows of a dataset are filter(), which changes which rows are present without changing their order, and arrange(), which changes the order of the rows without changing which are present. Both functions only affect the rows, and the columns are left unchanged. We’ll also discuss distinct() which finds rows with unique values but unlike arrange() and filter() it can also optionally modify the columns.

3.2.1 filter()

filter() allows you to keep rows based on the values of the columns1. The first argument is the data frame. The second and subsequent arguments are the conditions that must be true to keep the row. For example, we could find all flights that departed more than 120 minutes (two hours) late:

flights |> 
  filter(dep_delay > 120)

As well as > (greater than), you can use >= (greater than or equal to), < (less than), <= (less than or equal to), == (equal to), and != (not equal to). You can also combine conditions with & or , to indicate “and” (check for both conditions) or with | to indicate “or” (check for either condition):

# Flights that departed on January 1
flights |> 
  filter(month == 1 & day == 1)

# Flights that departed in January or February
flights |> 
  filter(month == 1 | month == 2)

There’s a useful shortcut when you’re combining | and ==: %in%. It keeps rows where the variable equals one of the values on the right:

# A shorter way to select flights that departed in January or February
flights |> 
  filter(month %in% c(1, 2))

We’ll come back to these comparisons and logical operators in more detail in Chapter 12.

When you run filter() dplyr executes the filtering operation, creating a new data frame, and then prints it. It doesn’t modify the existing flights dataset because dplyr functions never modify their inputs. To save the result, you need to use the assignment operator, <-:

jan1 <- flights |> 
  filter(month == 1 & day == 1)

3.2.2 Common mistakes

When you’re starting out with R, the easiest mistake to make is to use = instead of == when testing for equality. filter() will let you know when this happens:

flights |> 
  filter(month = 1)

Error in `filter()`:
! We detected a named input.
ℹ This usually means that you've used `=` instead of `==`.
ℹ Did you mean `month == 1`?
Backtrace:
 1. dplyr::filter(flights, month = 1)
 2. dplyr:::filter.data.frame(flights, month = 1)

Another mistakes is you write “or” statements like you would in English:

flights |> 
  filter(month == 1 | 2)

This “works”, in the sense that it doesn’t throw an error, but it doesn’t do what you want because | first checks the condition month == 1 and then checks the condition 2, which is not a sensible condition to check. We’ll learn more about what’s happening here and why in Section 15.6.2.

3.2.3 arrange()

arrange() changes the order of the rows based on the value of the columns. It takes a data frame and a set of column names (or more complicated expressions) to order by. If you provide more than one column name, each additional column will be used to break ties in the values of preceding columns. For example, the following code sorts by the departure time, which is spread over four columns. We get the earliest years first, then within a year the earliest months, etc.

flights |> 
  arrange(year, month, day, dep_time)

You can use desc() on a column inside of arrange() to re-order the data frame based on that column in descending (big-to-small) order. For example, this code orders flights from most to least delayed:

flights |> 
  arrange(desc(dep_delay))

3.2.4 distinct()

distinct() finds all the unique rows in a dataset, so in a technical sense, it primarily operates on the rows. Most of the time, however, you’ll want the distinct combination of some variables, so you can also optionally supply column names:

# Remove duplicate rows, if any
flights |> 
  distinct()

# Find all unique origin and destination pairs
flights |> 
  distinct(origin, dest)

Alternatively, if you want to the keep other columns when filtering for unique rows, you can use the .keep_all = TRUE option.

flights |> 
  distinct(origin, dest, .keep_all = TRUE)

It’s not a coincidence that all of these distinct flights are on January 1: distinct() will find the first occurrence of a unique row in the dataset and discard the rest.

If you want to find the number of occurrences instead, you’re better off swapping distinct() for count(), and with the sort = TRUE argument you can arrange them in descending order of number of occurrences. You’ll learn more about count in Section 13.3.

flights |>
  count(origin, dest, sort = TRUE)

3.2.5 Exercises

In a single pipeline for each condition, find all flights that meet the condition:
    Had an arrival delay of two or more hours
    Flew to Houston (IAH or HOU)
    Were operated by United, American, or Delta
    Departed in summer (July, August, and September)
    Arrived more than two hours late, but didn’t leave late
    Were delayed by at least an hour, but made up over 30 minutes in flight

flights |>
  filter(arr_delay >= 120 & dest %in% c("IAH", "HOU") & carrier %in% c(
"UA", "AA", "DL") & month %in% c(7, 8, 9)
  )

flights |>
  filter(arr_delay >= 120)

flights |>
  filter(dest %in% c("IAH", "HOU"))

flights |>
  filter(carrier %in% c("UA", "AA", "DL"))

flights |>
  filter(month %in% c(7,8,9))

flights |>
  filter(arr_delay > 120 & dep_delay <= 0)

flights |>
  filter(dep_delay >= 60 & arr_delay <= 30)

NA

Sort flights to find the flights with longest departure delays. Find the flights that left earliest in the morning.

flights |>
  arrange(desc(dep_delay))

flights |>
  arrange(dep_time)

NA
NA

Sort flights to find the fastest flights. (Hint: Try including a math calculation inside of your function.)

flights |>
  arrange(distance / air_time)

Was there a flight on every day of 2013? YES!

flights |>
  distinct(month, day)

Which flights traveled the farthest distance? Which traveled the least distance? JFK to Honolulu, Newark to La Guardia / Newark to Philadelphia.

flights |>
  arrange(desc(distance))

flights |>
  arrange(distance)

NA
NA

Does it matter what order you used filter() and arrange() if you’re using both? Why/why not? Think about the results and how much work the functions would have to do.

3.3 Columns

There are four important verbs that affect the columns without changing the rows: mutate() creates new columns that are derived from the existing columns, select() changes which columns are present, rename() changes the names of the columns, and relocate() changes the positions of the columns.

3.3.1 mutate()

The job of mutate() is to add new columns that are calculated from the existing columns. In the transform chapters, you’ll learn a large set of functions that you can use to manipulate different types of variables. For now, we’ll stick with basic algebra, which allows us to compute the gain, how much time a delayed flight made up in the air, and the speed in miles per hour:

By default, mutate() adds new columns on the right hand side of your dataset, which makes it difficult to see what’s happening here. We can use the .before argument to instead add the variables to the left hand side:

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    speed = distance / air_time * 60,
    .before = 1
  )

The . is a sign that .before is an argument to the function, not the name of a third new variable we are creating. You can also use .after to add after a variable, and in both .before and .after you can use the variable name instead of a position. For example, we could add the new variables after day:

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    speed = distance / air_time * 60,
    .after = day
  )

Alternatively, you can control which variables are kept with the .keep argument. A particularly useful argument is “used” which specifies that we only keep the columns that were involved or created in the mutate() step. For example, the following output will contain only the variables dep_delay, arr_delay, air_time, gain, hours, and gain_per_hour.

flights |> 
  mutate(
    gain = dep_delay - arr_delay,
    hours = air_time / 60,
    gain_per_hour = gain / hours,
    .keep = "used"
  )

Note that since we haven’t assigned the result of the above computation back to flights, the new variables gain, hours, and gain_per_hour will only be printed but will not be stored in a data frame. And if we want them to be available in a data frame for future use, we should think carefully about whether we want the result to be assigned back to flights, overwriting the original data frame with many more variables, or to a new object. Often, the right answer is a new object that is named informatively to indicate its contents, e.g., delay_gain, but you might also have good reasons for overwriting flights.

3.3.2 select()

It’s not uncommon to get datasets with hundreds or even thousands of variables. In this situation, the first challenge is often just focusing on the variables you’re interested in. select() allows you to rapidly zoom in on a useful subset using operations based on the names of the variables:

Select columns by name:

flights |>
  select(year, month, day)

Select all columns between year and day (inclusive):

flights |>
  select(year:day)

Select all columns except those from year to day (inclusive):

flights |>
  select(!year:day)

Historically this operation was done with - instead of !, so you’re likely to see that in the wild. These two operators serve the same purpose but with subtle differences in behavior. We recommend using ! because it reads as “not” and combines well with & and |.

Select all columns that are characters:

flights |> 
  select(where(is.character))

There are a number of helper functions you can use within select():

starts_with("abc"): matches names that begin with “abc”.
ends_with("xyz"): matches names that end with “xyz”.
contains("ijk"): matches names that contain “ijk”.
num_range("x", 1:3): matches x1, x2 and x3.

See ?select for more details. Once you know regular expressions (the topic of Chapter 15) you’ll also be able to use matches() to select variables that match a pattern.

You can rename variables as you select() them by using =. The new name appears on the left hand side of the =, and the old variable appears on the right hand side:

flights |> 
  select(tail_num = tailnum)

3.3.3 rename()

If you want to keep all the existing variables and just want to rename a few, you can use rename() instead of select():

flights |> 
  rename(tail_num = tailnum)

If you have a bunch of inconsistently named columns and it would be painful to fix them all by hand, check out janitor::clean_names() which provides some useful automated cleaning.

3.3.4 relocate()

Use relocate() to move variables around. You might want to collect related variables together or move important variables to the front. By default relocate() moves variables to the front:

flights |> 
  relocate(time_hour, air_time)

You can also specify where to put them using the .before and .after arguments, just like in mutate():

flights |> 
  relocate(year:dep_time, .after = time_hour)

flights |> 
  relocate(starts_with("arr"), .before = dep_time)

3.3.5 Exercises

Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?

flights |>
  select(dep_time, sched_dep_time, dep_delay)

NA
NA

Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.

flights |>
  select(starts_with(c("dep", "arr")))

flights |>
  select(4, 6, 7, 9)

NA
NA
NA
NA

What happens if you specify the name of the same variable multiple times in a select() call?

flights |>
  select(dep_time, dep_time)

What does the any_of() function do? Why might it be helpful in conjunction with this vector?

variables <- c("year", "month", "day", "dep_delay", "arr_delay")
flights|>
  select(any_of(variables))

flights|>
  select(all_of(variables))

Does the result of running the following code surprise you? How do the select helpers deal with upper and lower case by default? How can you change that default?

flights |> select(contains("TIME"))

flights |> select(contains("TIME", ignore.case=FALSE))

NA

Rename air_time to air_time_min to indicate units of measurement and move it to the beginning of the data frame.

flights|>
  relocate(air_time_min = air_time)

Why doesn’t the following work, and what does the error mean?

flights |> 
  select(tailnum) |> 
  arrange(arr_delay)

Error in `arrange()`:
ℹ In argument: `..1 = arr_delay`.
Caused by error:
! object 'arr_delay' not found
Backtrace:
  1. dplyr::arrange(select(flights, tailnum), arr_delay)
  2. dplyr:::arrange.data.frame(select(flights, tailnum), arr_delay)
  3. dplyr:::arrange_rows(.data, dots = dots, locale = .locale)
  5. dplyr:::mutate.data.frame(data, `:=`("{name}", !!dot), .keep = "none")
  6. dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
  8. dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
  9. mask$eval_all_mutate(quo)
 10. dplyr (local) eval()

3.4 The pipe

We’ve shown you simple examples of the pipe above, but its real power arises when you start to combine multiple verbs. For example, imagine that you wanted to find the fastest flights to Houston’s IAH airport: you need to combine filter(), mutate(), select(), and arrange():

flights |> 
  filter(dest == "IAH") |> 
  mutate(speed = distance / air_time * 60) |> 
  select(year:day, dep_time, carrier, flight, speed) |> 
  arrange(desc(speed))

3.5 Groups

So far you’ve learned about functions that work with rows and columns. dplyr gets even more powerful when you add in the ability to work with groups. In this section, we’ll focus on the most important functions: group_by(), summarize(), and the slice family of functions.

3.5.1 group_by()

Use group_by() to divide your dataset into groups meaningful for your analysis:

flights |> 
  group_by(month)

group_by() doesn’t change the data but, if you look closely at the output, you’ll notice that the output indicates that it is “grouped by” month (Groups: month [12]). This means subsequent operations will now work “by month”. group_by() adds this grouped feature (referred to as class) to the data frame, which changes the behavior of the subsequent verbs applied to the data.

3.5.2 summarize()

The most important grouped operation is a summary, which, if being used to calculate a single summary statistic, reduces the data frame to have a single row for each group. In dplyr, this operation is performed by summarize()3, as shown by the following example, which computes the average departure delay by month:

flights |> 
  group_by(month) |> 
  summarize(
    avg_delay = mean(dep_delay, na.rm=TRUE)
  )

You can create any number of summaries in a single call to summarize(). You’ll learn various useful summaries in the upcoming chapters, but one very useful summary is n(), which returns the number of rows in each group:

flights |> 
  group_by(month) |> 
  summarize(
    avg_delay = mean(dep_delay, na.rm = TRUE), 
    n = n()
  )

3.5.3 The slice_ functions

There are five handy functions that allow you extract specific rows within each group:

df |> slice_head(n = 1) takes the first row from each group.
df |> slice_tail(n = 1) takes the last row in each group.
df |> slice_min(x, n = 1) takes the row with the smallest value of column x.
df |> slice_max(x, n = 1) takes the row with the largest value of column x.
df |> slice_sample(n = 1) takes one random row.

You can vary n to select more than one row, or instead of n =, you can use prop = 0.1 to select (e.g.) 10% of the rows in each group. For example, the following code finds the flights that are most delayed upon arrival at each destination:

flights |> 
  group_by(dest) |> 
  slice_max(arr_delay, n = 1) |>
  relocate(dest)

Note that there are 105 destinations but we get 108 rows here. What’s up? slice_min() and slice_max() keep tied values so n = 1 means give us all rows with the highest value. If you want exactly one row per group you can set with_ties = FALSE.

This is similar to computing the max delay with summarize(), but you get the whole corresponding row (or rows if there’s a tie) instead of the single summary statistic.

3.5.4 Grouping by multiple variables

You can create groups using more than one variable. For example, we could make a group for each date.

daily <- flights |>  
  group_by(year, month, day)
daily

When you summarize a tibble grouped by more than one variable, each summary peels off the last group. In hindsight, this wasn’t a great way to make this function work, but it’s difficult to change without breaking existing code. To make it obvious what’s happening, dplyr displays a message that tells you how you can change this behavior:

daily_flights <- daily |> 
  summarize(n = n())

`summarise()` has grouped output by 'year', 'month'. You can override using the `.groups` argument.

If you’re happy with this behavior, you can explicitly request it in order to suppress the message:

daily_flights <- daily |> 
  summarize(
    n = n(), 
    .groups = "drop_last"
  )

Alternatively, change the default behavior by setting a different value, e.g., “drop” to drop all grouping or “keep” to preserve the same groups.

3.5.5 Ungrouping

You might also want to remove grouping from a data frame without using summarize(). You can do this with ungroup().

daily |> 
  ungroup()

Now let’s see what happens when you summarize an ungrouped data frame.

daily |> 
  ungroup() |>
  summarize(
    avg_delay = mean(dep_delay, na.rm = TRUE), 
    flights = n()
  )

You get a single row back because dplyr treats all the rows in an ungrouped data frame as belonging to one group.

3.5.6 .by

dplyr 1.1.0 includes a new, experimental, syntax for per-operation grouping, the .by argument. group_by() and ungroup() aren’t going away, but you can now also use the .by argument to group within a single operation:

flights |> 
  summarize(
    delay = mean(dep_delay, na.rm = TRUE), 
    n = n(),
    .by = month
  )

Or if you want to group by multiple variables:

flights |> 
  summarize(
    delay = mean(dep_delay, na.rm = TRUE), 
    n = n(),
    .by = c(origin, dest)
  )

.by works with all verbs and has the advantage that you don’t need to use the .groups argument to suppress the grouping message or ungroup() when you’re done.

We didn’t focus on this syntax in this chapter because it was very new when we wrote the book. We did want to mention it because we think it has a lot of promise and it’s likely to be quite popular. You can learn more about it in the dplyr 1.1.0 blog post.

3.5.7 Exercises

Which carrier has the worst average delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights |> group_by(carrier, dest) |> summarize(n()))

flights |> 
  group_by(carrier) |> 
  summarize(n(),
    avg_delay = mean(dep_delay, na.rm = TRUE)) |> 
  arrange(desc(avg_delay))

flights |> 
  group_by(dest) |> 
  summarize(n(),
    avg_delay = mean(dep_delay, na.rm = TRUE)) |> 
  arrange(desc(avg_delay))

flights |> 
  group_by(carrier,dest) |> 
  summarize(n(),
            avg_delay = mean(dep_delay, na.rm=TRUE)) |> 
  arrange(desc(avg_delay))

`summarise()` has grouped output by 'carrier'. You can override using the `.groups` argument.

Find the flights that are most delayed upon departure from each destination.

flights |> 
  relocate(dest, dep_delay) |> 
  group_by(dest) |> 
  slice_max(dep_delay, n=1)

NA

How do delays vary over the course of the day. Illustrate your answer with a plot.